We propose and study the problem of generative multi-agent behavioral
cloning, where the goal is to learn a generative multi-agent policy from
pre-collected demonstration data. Building upon advances in deep generative
models, we present a hierarchical policy framework that can tractably learn
complex mappings from input states to distributions over multi-agent action
spaces. Our framework is flexible and can incorporate high-level domain
knowledge into the structure of the underlying deep graphical model. For
instance, we can effectively learn low-dimensional structures, such as
long-term goals and team coordination, from data. Thus, an additional benefit
of our hierarchical approach is the ability to plan over multiple time scales
for effective long-term planning. We showcase our approach in an application of
modeling team offensive play from basketball tracking data. We show how to
instantiate our framework to effectively model complex interactions between
basketball players and generate realistic multi-agent trajectories of
basketball gameplay over long time periods. We validate our approach using both
quantitative and qualitative evaluations, including a user study comparison
conducted with professional sports analysts.

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